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Record Nr. |
UNINA9910480695103321 |
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Autore |
Boyle Mike <1951-> |
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Titolo |
Resolving maps and the dimension group for shifts of finite type / / Mike Boyle, Brian Marcus, and Paul Trow |
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Pubbl/distr/stampa |
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Providence, Rhode Island : , : American Mathematical Society, , 1987 |
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©1987 |
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ISBN |
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Descrizione fisica |
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1 online resource (157 p.) |
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Collana |
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Memoirs of the American Mathematical Society, , 0065-9266 ; ; Volume 70, Number 377 |
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Disciplina |
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Soggetti |
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Topological dynamics |
Entropy (Information theory) |
Mappings (Mathematics) |
Electronic books. |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Note generali |
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"November 1978, Volume 70, Number 377 (fifith of 6 numbers)." |
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Nota di bibliografia |
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Includes bibliographical references and index. |
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Nota di contenuto |
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""Table of Contents""; ""1. Introduction""; ""2. Shifts of Finite Type, Factor Maps and the Dimension Group""; ""3. Eventual Factors Theorem""; ""4. Eventual Factors for Sofic Systems""; ""5. Linear Maps, Ideal Classes, and Realization""; ""6. Common Factors""; ""7. Common Extensions""; ""8. Regular Isomorphism""; ""9. Duality""; ""10. Compositions of Closing Maps""; ""11. Topological Construction of Dimension Groups""; ""12. Stable Factor Maps""; ""13. References""; ""14. Index""; ""15. List of Symbols"" |
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2. |
Record Nr. |
UNISA996418313003316 |
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Titolo |
Artificial Intelligence and Machine Learning for Digital Pathology [[electronic resource] ] : State-of-the-Art and Future Challenges / / edited by Andreas Holzinger, Randy Goebel, Michael Mengel, Heimo Müller |
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Pubbl/distr/stampa |
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Cham : , : Springer International Publishing : , : Imprint : Springer, , 2020 |
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ISBN |
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Edizione |
[1st ed. 2020.] |
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Descrizione fisica |
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1 online resource (xii, 339 pages) |
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Collana |
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Lecture Notes in Artificial Intelligence ; ; 12090 |
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Disciplina |
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Soggetti |
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Artificial intelligence |
Computers |
Database management |
Application software |
Computer security |
Optical data processing |
Artificial Intelligence |
Computing Milieux |
Database Management |
Computer Applications |
Systems and Data Security |
Image Processing and Computer Vision |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Note generali |
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Nota di contenuto |
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Expectations of Artificial Intelligence for Pathology -- Interpretable Deep Neural Network to Predict Estrogen Receptor Status from Haematoxylin-Eosin Images -- Supporting the Donation of Health Records to Biobanks for Medical Research -- Survey of XAI in Digital Pathology -- Sample Quality as Basic Prerequisite for Data Quality: A Quality Management System for Biobanks -- Black Box Nature of Deep Learning for Digital Pathology: Beyond Quantitative to Qualitative Algorithmic Performances -- Towards a Better Understanding of the |
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Workflows: Modeling Pathology Processes in View of Future AI Integration -- OBDEX – Open Block Data Exchange System -- Image Processing and Machine Learning Techniques for Diabetic Retinopathy Detection: A Review -- Higher Education Teaching Material on Machine Learning in the Domain of Digital Pathology -- Classification vs Deep Learning in Cancer Degree on Limited Histopathology Datasets -- Biobanks and Biobank-Based Artificial Intelligence (AI) Implementation Through an International Lens -- HistoMapr: An Explainable AI (xAI) Platform for Computational Pathology Solutions -- Extension of the Identity Management System Mainzelliste to Reduce Runtimes for Patient Registration in Large Datasets -- Digital Image Analysis in Pathology Using DNA Stain: Contributions in Cancer Diagnostics and Development of Prognostic and Theranostic Biomarkers -- Assessment and Comparison of Colour Fidelity of Whole slide imaging scanners -- Deep Learning Methods for Mitosis Detection in Breast Cancer Histopathological Images: a Comprehensive Review -- Developments in AI and Machine Learning for Neuroimaging. |
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Sommario/riassunto |
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Data driven Artificial Intelligence (AI) and Machine Learning (ML) in digital pathology, radiology, and dermatology is very promising. In specific cases, for example, Deep Learning (DL), even exceeding human performance. However, in the context of medicine it is important for a human expert to verify the outcome. Consequently, there is a need for transparency and re-traceability of state-of-the-art solutions to make them usable for ethical responsible medical decision support. Moreover, big data is required for training, covering a wide spectrum of a variety of human diseases in different organ systems. These data sets must meet top-quality and regulatory criteria and must be well annotated for ML at patient-, sample-, and image-level. Here biobanks play a central and future role in providing large collections of high-quality, well-annotated samples and data. The main challenges are finding biobanks containing ‘‘fit-for-purpose’’ samples, providing quality related meta-data, gaining access to standardized medical data and annotations, and mass scanning of whole slides including efficient data management solutions. |
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